How to Use the Tomorrow.io Extended MCP in Vercel AI SDK

Q: How does Tomorrow.io Extended integrate with Vercel AI SDK?

You call mcpClient.tools() to get the available MCP Server functions. Then, you pass those tools into your streaming text generation function. The data streams directly into your client's UI.

Q: Can I handle alerts with Tomorrow.io Extended on Vercel AI SDK?

Yes. You use the listweatheralerts tool to pull current advisories. The results are streamed live, letting your user see exactly what warnings are active in that area.

Q: Is the weather history accessible with Tomorrow.io Extended on Vercel AI SDK?

Absolutely. The getrecenthistory tool retrieves past conditions, letting you populate graphs or show users what the area looked like yesterday.

Stream hyper-local weather and air quality data directly into your Vercel AI SDK frontends.

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Connect Tomorrow.io Extended MCP to Vercel AI SDK

Create your Vinkius account to connect Tomorrow.io Extended to Vercel AI SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Tomorrow.io Extended with Vercel AI SDK

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Show live `get_realtime_weather` updates in Vercel AI SDK.

The `get_realtime_weather` tool provides current atmospheric conditions. When you stream this data using the Vercel AI SDK, your users see the weather metrics appear instantly, without a loading spinner. This is crucial for building frontends where users expect immediate feedback. You can show real-time updates on wind speed or temperature as they come in.

Build interactive air quality checks with MCP Server.

Use the `get_air_quality` tool to fetch hyper-local air metrics. Passing this result into your streaming text endpoint allows you to build live, data-driven health widgets for your users. You get clean JSON that shows specific pollutant levels (like PM2.5), letting you display actionable warnings right in the user interface.

Display weather alerts and forecasts with Vercel AI SDK.

The `list_weather_alerts` tool gathers current local danger advisories, while `get_weather_forecast` predicts future conditions. Streaming both of these allows your agent to present a complete picture—what's happening now and what's coming. This combination lets you build sophisticated product views that guide users through immediate risks and planned changes.

Setup guide

Set up Tomorrow.io Extended MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Tomorrow.io Extended tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Tomorrow.io Extended transactions",
});

console.log(text);
await mcpClient.close();

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Common questions about Tomorrow.io Extended MCP in Vercel AI SDK

You call `mcpClient.tools()` to get the available MCP Server functions. Then, you pass those tools into your streaming text generation function. The data streams directly into your client's UI.

This server provides air quality data, real-time weather metrics, historical summaries via `get_recent_history`, and future predictions using `get_weather_forecast`.

Yes. You use the `list_weather_alerts` tool to pull current advisories. The results are streamed live, letting your user see exactly what warnings are active in that area.

This MCP Server primarily touches public weather and air quality data types. You don't need to worry about sensitive user identifiers when fetching this information.

Absolutely. The `get_recent_history` tool retrieves past conditions, letting you populate graphs or show users what the area looked like yesterday.

Use it with your favorite AI tools

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